Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6412
Title: Comparison of different strategies of utilizing fuzzy clustering in structure identification
Authors: Kılıç, Kemal
Uncu, Öze
Türkşen, İsmail Burhan
Keywords: fuzzy system modelling
medicine
knowledge acquisition
data mining
structure identification
Issue Date: 2007
Publisher: Elsevier Science Inc
Abstract: Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and offer a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classification problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches. (C) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.ins.2007.06.030
https://hdl.handle.net/20.500.11851/6412
ISSN: 0020-0255
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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